Capacity Planning for Effective Cohorting of Dialysis Patients during the Coronavirus Pandemic: A Case Study

TL;DR

This paper presents a stochastic programming model to optimize capacity planning for a dialysis clinic during COVID-19, aiming to minimize infection risk through effective cohorting and resource allocation.

Contribution

It introduces a novel two-stage stochastic programming approach for capacity planning in dialysis clinics under pandemic uncertainties.

Findings

The model effectively supports capacity decisions based on real patient data.

Cohorting strategies significantly reduce infection overlap among patients.

The approach improves resource utilization during pandemic conditions.

Abstract

Chronic dialysis patients have been among the most vulnerable groups of the society during the coronavirus (COVID-19) pandemic as they need regular treatments in a hospital environment, facing infection risk. Moreover, the demand for dialysis resources has significantly increased since many COVID-19 patients need acute dialysis due to kidney failure. In this study, we address capacity planning decisions of a hemodialysis clinic located within a major hospital in Istanbul, designated to serve both infected and uninfected patients during the pandemic with limited resources (i.e., dialysis machines). The hemodialysis clinic applies a three-unit cohorting strategy to treat four types of patients in separate units and at different times to mitigate infection spread risk among patients. Accordingly, at the beginning of each week, the clinic needs to determine the number of available dialysis machines to allocate to each unit that serves different patient cohorts. Given the uncertainties in the number of different types of patients that will need dialysis, it is a challenge to allocate the scarce dialysis resources effectively by evaluating which capacity configuration would minimize the overlapping treatment sessions of infected and uninfected patients over a week. We represent the uncertainties in the number of patients by a set of scenarios and present a two-stage stochastic programming approach to support capacity allocation decisions of the clinic. We present a case study based on the real-world patient data obtained from the clinic to illustrate the effectiveness of the proposed modeling approach and compare the performance of different cohorting strategies.